In order to examine a subject's organs, it is often helpful to represent these organs by way of imaging. Magnetic resonance (MR) tomography is a suitable method for examining ventricles of the heart, and studies have shown that more reproducible and more accurate measurement of the volume and mass of a ventricle can be carried out with this imaging technique than with other imaging techniques. For MR imaging of the ventricular function, for example, two-dimensional images which show the movement of the heart at short time intervals (for example 40 ms) are recorded over a particular duration. These images can be recorded while the subject is holding his or her breath or while the subject is breathing freely.
The recording of magnetic resonance signals may furthermore be initiated by triggering, for example recording triggered by an electrocardiogram (ECG), or the magnetic resonance signals may be recorded without triggering. For evaluation of the image data reconstructed from the recorded magnetic resonance signals, it is necessary to segment the left ventricle in the image data.
There are a number of methods for segmenting the left heart valve in magnetic resonance images. The left ventricle may for example be segmented by a method which combines edges, regions and shape information (M. P. Jolly, “Combining Edge, Region and Shape Information to Segment the Left Ventricle in Cardiac MR Images”, Proc. Medical Image Computing and Computer-Assisted Intervention, The Netherlands, 2001, 482-490). Another conventional method uses a finite element model, which represents the geometry of the left ventricle, and combines this with points which have been input by a user in order to obtain an estimate of the boundaries of the left ventricle (A. A. Young et al., “Left Ventricular Mass and Volume: Fast Calculation with Guide-Point Modeling on MR images”, Radiology 2000; 216: 597-602). In M. Kaus et al., “Automated segmentation of the left ventricle in cardiac MRI”, Medical Image Analysis, 2004; 8:245-254, a deformable shape model was used in order to carry out heart muscle segmentation fully automatically. The methods described above, however, have some essential disadvantages which will be described below.
Often, reliable examination of the ventricular function and in particular the evaluation of parameters, for example the volume, is made difficult by the fact that it is hard for a subject to hold their breath when a time series of layer images is being recorded. In order to overcome this problem, magnetic resonance images of the cardiac function may be recorded while the subject is breathing freely. Available methods for such recording are realtime imaging, breath triggering, self-triggering (for example based on MR image data) or “navigator gating”, in which breathing-induced movement is determined and the image data are subsequently corrected for this. Realtime images are for example complete images which are recorded within a short duration, for example 60 ms.
Such imaging techniques are becoming ever more important for the examination of cardiovascular function, since they provide images with a quality which is sufficient for medical diagnosis, and these images of subjects can be recorded while they are breathing freely. Such techniques are advantageous particularly for patients who have difficulty in holding their breath. With conventional methods, it is not however possible for image data, recorded without breath triggering while the subject is breathing, to be segmented for quantitative analysis. The lack of suitable methods is essentially due to a lack of reliable image registering or processing tools with which different layers, recorded in various breathing positions, can be aligned. Conventional image registering methods are very computer-intensive and often lead to modification of the image data, for example by interpolation, which is likewise disadvantageous.
A conventional solution for overcoming this problem is so-called “navigator gating”, in which so-called registering of image data is not in principle necessary (D. C. Peters et al., “2D Cardiac Function during Free-breathing with Navigators”, Proc. International Society for Magnetic Resonance in Medicine 2007, 3860). With this technique, however, the times taken to record image data are substantially longer compared with the recording time for the same volume with realtime imaging (for example 10 minutes with navigator gating compared with 2 minutes for realtime recording).